Systems and computer-implemented processes for occupational risk assessment

ABSTRACT

Systems and methods are disclosed for determine creditworthiness based on the stability of the customer&#39;s occupation. Occupational risk scores may be used to group stable or unstable occupations and may be used in conjunction with other indicators of creditworthiness. Risk band scores may also be used to determine creditworthiness. Other aspects of the disclosed embodiments are described herein.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.61/734,513, filed Dec. 7, 2012, the entire contents of which is herebyincorporated by reference.

FIELD

The disclosed embodiments generally relate to underwriting processes forfinancial accounts, and more particularly, to processes and systems forusing occupational data to enhance risk analysis for determining creditlines and other aspects of financial accounts.

BACKGROUND

Currently, financial account providers, such as banks, credit cardcompanies, merchants, lenders, and the like, analyze risk associatedwith a current or potential customer using such information as thecustomer's credit score, household income, assets, etc. Theseconsiderations may provide for an adequate assessment of the customer'spresent situation and ability to repay debt, but they provide limitedinsight as to the long-term ability of the customer to repay debt.

Creditworthiness is important to customers because it has an impact ontheir ability to open financial accounts, obtain or increase lines ofcredit, obtain certain interest rates, etc. A stable occupation is anasset to customers that could be treated as a positive indicator ofcreditworthiness; however, financial institutions do not currently usethis information to determine creditworthiness.

In many credit applications, financial account providers requestidentification of the customer's current occupation. But beyond,perhaps, a rudimentary review that the customer is employed and thattheir reported annual income is consistent with their occupation, thisinformation is largely unused by the financial account providers.

Thus, existing mechanisms fail to appreciate the asset a stableoccupation represents to a consumer. Existing mechanisms are alsolimited in their ability to determine the long-term ability of acustomer to repay debt. It is therefore desirable to provide a systemand process that incorporates data on the stability of an occupation indetermining the creditworthiness of a customer. Many benefits may beobtained by using occupational stability data in addition to traditionalmethods of determining credit worthiness. Such benefits may include,among others, savings from better prediction of credit risks andoptimization of customer lines of credit. Using disclosed embodiments,financial account providers may also benefit from gains due to upmarketcredit limit increases (e.g., increasing credit limits for upmarketcustomers) and graduation type programs (e.g., moving customers frommain street type products to upmarket type products). The disclosedembodiments may be beneficial to many industries. For example,occupational stability data may be used to provide insurance providers abetter understanding of a customer's ability to pay deductibles orpremium installments for insurance products, which in turn wouldinfluence the premium calculations used by the insurance provider todetermine the installment payment schedule.

SUMMARY

Disclosed embodiments include systems and processes for enabling afinancial account provider to estimate the creditworthiness, andparticularly the future creditworthiness, of a customer based on theoccupation of the customer. It is to be understood that a customer maybe a current customer of the financial provider or a potential customerof the financial service provider. The disclosed embodiments include,among other things, mechanisms for assisting in the gathering ofinformation regarding the occupation of the customer and indicators ofstability for various occupations, as well as mechanisms that assist indetermining the stability of the occupation of a particular customer.

In certain embodiments, a computer-implemented method for determiningcreditworthiness of a customer is provided. The method may comprisereceiving, by a processor, a list of indicators of stability for variousoccupations and designating risk bands for the various indicators ofstability. Risk band scores may be assigned to occupations based on theindicators of stability associated with each occupation. The risk bandscores may be used to generate an occupational risk score for each ofthe occupations. The method may further comprise receiving a customer'soccupation information (or a set of occupation information from morethan one customer). The customer occupation information may be matchedto one of the occupations, and the occupational risk score for thatoccupation may be assigned to the customer occupation information. Theassigned occupational risk score may be used to calculate thecreditworthiness of the customer.

In certain embodiments, a financial account provider system fordetermining creditworthiness of a customer is provided. The system mayinclude a storage device storing instructions and a processor configuredto execute those instructions. The system may be configured to receive alist of indicators of stability for one or more occupations anddesignate risk bands for one or more of the indicators of stability. Thesystem may be further configured to assign risk band scores to theoccupations based on the indicators of stability associated with theoccupations and generate occupational risk scores based on the risk bandscores assigned to the occupations. The system may also be configured toreceive a customer's occupation information (or a set of occupationinformation from more than one customer), match the customer occupationinformation with one of the occupations, and assign the occupationalrisk score for that occupation to the customer occupation information.The assigned occupational risk score may be used to calculate thecreditworthiness of the customer.

Consistent with other disclosed embodiments, tangible computer-readablestorage media may store program instructions executable by one or moreprocessors to implement any of the processes disclosed herein.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments and, togetherwith the description, serve to explain the disclosed principles. In thedrawings:

FIG. 1 is an exemplary system that may be used to implement thedisclosed embodiments.

FIG. 2 is a flowchart of an exemplary occupation risk assessment processconsistent with the disclosed embodiments.

FIG. 3 is a flowchart of an exemplary data interpreting processconsistent with the disclosed embodiments.

FIG. 4A is an exemplary risk band matrix consistent with the disclosedembodiments.

FIG. 4B is an exemplary risk score chart consistent with the disclosedembodiments.

FIG. 5 is a flowchart of an exemplary occupation title interpreterconsistent with the disclosed embodiments.

FIG. 6A is an example of orthogonal risk splitting between overallunstable occupations and overall stable occupations that can be obtainedfrom occupational data to determine creditworthiness, consistent withthe disclosed embodiments.

FIG. 6B is an example of the orthogonal risk splitting between unstableoccupations and stable occupations for occupations with high incomesthat can be obtained from occupational data to determinecreditworthiness, consistent with the disclosed embodiments.

FIG. 7A is an exemplary graph of the percentage of open accounts thatcharge-off in any one month (i.e., percentage of ‘bad’ accounts for amonth compared to number of open accounts that month or “Pbad per open”)for accounts associated with FICO scores and occupation stability forcustomers with annual incomes of over $80,000.

FIG. 7B provides an exemplary graph of the distribution of stable andunstable occupations over FICO bands.

FIG. 7C is an exemplary graph depicting the stability of occupation withrespect to FICO scores.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to exemplary embodiments, examplesof which are illustrated in the accompanying drawings and disclosedherein. Wherever convenient, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

FIG. 1 is an exemplary system configured to perform one or more softwareprocesses that, when executed, provide one or more aspects of thedisclosed embodiments. The components and arrangement shown in FIG. 1are not intended to be limiting to any disclosed embodiment, as thecomponents used to implement the processes and features disclosed heremay vary.

In accordance with certain disclosed embodiments, system 100 may beprovided that includes one or more clients 102 (associated with one ormore customers 101), network 103, financial account provider system 104,customer occupation information database 105, and one or moreoccupational databases 106. Other components known to one of ordinaryskill in the art may be included in system 100 to process, transmit,provide, and receive information consistent with the disclosedembodiments.

Financial account provider system 104 may be a computer-based systemincluding computer system components, such as one or more servers,desktop computers, workstations, tablets, hand held computing devices,memory devices, and/or internal network(s) connecting the components. Inone embodiment, financial account provider system 104 may be a serverthat includes one or more processors 107, memory devices, such as memory108, and interface components 109. Financial account provider system 104may be a single server or may be configured as a distributed computersystem including multiple servers or computers that interoperate toperform one or more of the processes and functionalities associated withthe disclosed embodiments. In certain embodiments, financial accountprovider system 104 may be a server associated with financial accountprovider 110, such as, for example, a bank, lender, merchant, creditcard provider, an insurance provider, or any other entity that providesfinancial accounts to customers. Financial accounts may include, forexample, credit card accounts, checking accounts, savings accounts,loans, investment accounts, insurance accounts, or any other type ofaccount relating to financial products. In one aspect, financial accountprovider 110 may be a provider that advertises, solicits, or otherwisecommunicates with potential customers 101 to offer financial accounts ornegotiate changes to existing financial accounts to customers 101provided by financial account provider 110.

Processor(s) 107 may be one or more known processing devices, such as amicroprocessor from the Pentium™ family manufactured by Intel™ or theTurion™ family manufactured by AMD™. Processor(s) 107 may include asingle core or multiple core processor system that provides the abilityto perform parallel processes simultaneously. For example, processor 107may be a single core processor that is configured with virtualprocessing technologies known to those skilled in the art. In certainembodiments, processor 107 may use logical processors to simultaneouslyexecute and control multiple processes. Processor 107 may implementvirtual machine technologies, or other similar known technologies toprovide the ability to execute, control, run, manipulate, store, etc.multiple software processes, applications, programs, etc. In anotherembodiment, processor(s) 107 may include a multiple-core processorarrangement (e.g., dual or quad core) that is configured to provideparallel processing functionalities to allow financial account providersystem 104 to execute multiple processes simultaneously. One of ordinaryskill in the art would understand that other types of processorarrangements could be implemented that provide for the capabilitiesdisclosed herein.

Financial account provider system 104 may include one or more storagedevices configured to store information used by processor 107 (or othercomponents) to perform certain functions related to the disclosedembodiments. In one example, financial account provider system 104 mayinclude a memory 108 that includes instructions to enable processor(s)107 to execute one or more applications, such as server applications,network communication processes, and any other type of application orsoftware known to be available on computer systems. Alternatively, theinstructions, application programs, etc. may be stored in an externalstorage or available from memory over a network. The one or more storagedevices may be a volatile or non-volatile, magnetic, semiconductor,tape, optical, removable, nonremovable, or other type of storage deviceor tangible computer-readable medium.

In one embodiment, financial account provider system 104 includes memory108 that includes instructions that, when executed by processor(s) 107,perform one or more processes consistent with the functionalitiesdisclosed herein. Methods, systems, and articles of manufactureconsistent with disclosed embodiments are not limited to separateprograms or computers configured to perform dedicated tasks. Forexample, financial account provider system 104 may include a memory thatmay include one or more programs to perform one or more functions ofinterface components 109. Moreover, processor(s) 107 may execute one ormore programs located remotely from system 100. For example, system 100may access one or more remote programs, that, when executed, performfunctions related to disclosed embodiments. Memory 108 may include oneor more memory devices that store data and instructions used to performone or more features of the disclosed embodiments. Memory 108 may alsoinclude any combination of one or more databases controlled by memorycontroller devices (e.g., server(s), etc.) or software, such as documentmanagement systems, Microsoft SQL databases, Share Point databases,Oracle™ databases, Sybase™ databases, or other relational databases.

In certain embodiments, memory 108 may include software components that,when executed by processor(s) 107, perform one or more processesconsistent with the disclosed embodiments. For example, memory 108 mayinclude data miner 111, data interpreter 112, risk band matrix 113, riskscore engine 114, occupation title interpreter 115, and creditworthinessengine 116, which include software instructions executable by one ormore processors, such as processor(s) 107, alone or in variouscombinations.

In certain embodiments, data miner 111 may be configured to performfunctions that find, copy, transmit, and store information onoccupations and indicators of stability from external sources, such asoccupational database 106, to financial account provider system 104.Data interpreter may be configured to clean and format occupational andindicators of stability data from various sources (such as occupationaldatabase 106) to remove errors in the data, consolidate data, and createmore uniform data. Risk band matrix 113 may be configured to create andstore the parameters for indicators of stability that define variousrisk bands (described further in FIGS. 2, 4A, and related text). Riskscore engine 114 may be configured to calculate and store the riskscores associated with various occupations (described further in FIGS.2, 4B, and related text). Risk score engine 114 may also be configuredto categorize occupations by risk score and store the categorizationinformation. Occupation title interpreter 115 may be configured to cleanand format occupation titles from various sources (such as customeroccupation information database 105 or client 102) to remove errors inthe title data and compare the received occupational title to the listof occupations with indicators of stability information.Creditworthiness engine 116 may be configured to determine thecreditworthiness of customer 101 (or customers associated with customeroccupation information database 105) using the occupational risk bandscores, occupational risk scores, or a combination of the occupationalrisk band scores, occupational risk scores and other creditworthinessscores for the customer 101.

Financial account provider system 104 may also be communicativelyconnected to one or more memory devices (e.g., customer occupationinformation database 105, occupational database 106, or other databases(not shown) locally or through a network). In certain embodiments,customer occupation information database 105 stores information aboutcurrent customers or potential customers, including occupation titledata that may be accessed by financial account provider system 104.Customer occupation information database 105 may be associated withfinancial account provider 110. For example, financial account provider110 may be interested in increasing lines of credit for certain of itscurrent customers which are low credit risks based on occupationinformation collected about its current customers. The remote memorydevices may be configured to store information and may be accessedand/or managed by financial account provider system 104. By way ofexample, the remote memory devices may employ a document managementsystem. Systems and methods of disclosed embodiments, however, are notlimited to separate databases or even to the use of a database.

Financial account provider system 104 may also include one or more I/Odevices that may comprise one or more interfaces for receiving signals,or input from input devices, and providing signals or output to one ormore output devices that allow data to be received and/or transmitted byfinancial account provider system 104. For example, financial accountprovider system 104 may include interface components 109 that mayprovide interfaces to one or more input devices. Input devices mayinclude, for example, one or more keyboards, mouse devices, and thelike, that enable financial account provider system 104 to receive datafrom one or more users, such as customers 101 using clients 102 orrepresentatives of financial account provider 110. Further, interfacecomponents 109 may include components configured to send and receiveinformation between components of financial account provider system 104,customer occupation information database 105, clients 102, andoccupational database 106. Interface components 109 may interface withother components or devices through network 103.

Network 103 may be any type of network that provides communications,exchanges information, and/or facilitates the exchange of informationbetween Financial account provider system 104 and client 102. In oneembodiment, network 103 may be the Internet, a Local Area Network, orother suitable connection(s) that enables system 100 to send and receiveinformation between the components of system 100.

Client 102 may each be one or more computer systems associated with orused by one or more customers 101. For example, client 102 may include ageneral purpose or notebook computer, a mobile device with computingability, a server, a desktop computer, tablet, or any combination ofthese computers and/or affiliated components. In one embodiment, eachclient may be a computer system or device that is operated by customer101 who may be a current customer or a potential customer of financialservice provider 110. Client 102 may be configured with storage thatstores one or more operating systems that perform known operating systemfunctions when executed by one or more processors. By way of example,the operating systems may include Microsoft Windows™, Unix™, Linux™, orApple™ type operating systems. The operating systems may also includePersonal Digital Assistant (PDA) type operating systems, such asMicrosoft CE™, Apple iOS, or other types of operating systems.Embodiments of the disclosed invention will operate and function withcomputer systems running any type of operating system. Client 102 mayalso include communication software that, when executed by a processor,provides communications with network 103, such as Web browser software,tablet or smart hand held device networking software, etc. Accordingly,client 102 may be a device that executes mobile applications, such as atablet or mobile device.

FIG. 2 depicts a flowchart providing an exemplary overview of theoccupation risk assessment process. As shown in process 200, indicatorsof stability for a list of occupations may be received (step 201). Theindicators may be stored in memory 108 operated by financial accountprovider 110. Indicators of stability may include, for example, averageannual income for an occupation, average highest educational levelattained for the occupation, percentage of persons with variouseducational histories for the occupation, average unemployment rate forthat occupation, change in unemployment rate over a period of time(e.g., during an economic downturn, for example), etc. FIG. 4A providesan exemplary risk band matrix for various indicators of stability401-407. Additional advantageous indicators of stability for occupationsmay also be considered. For example, geographic location may affect thestability of an occupation (e.g., construction workers in theWashington, D.C. area may have more a more stable occupation thanconstruction workers in the Las Vegas, Nev. area). The list ofoccupations and associated indicators of stability may be created by theentity that operates process 200 (e.g., financial account provider 110).Alternatively, lists of occupations and associated indicators ofstability may be obtained, via network 103, from various public andprivate occupational databases 106 (see also, data miner 111 and relatedtext below). For example, the Bureau of Labor and Statistics maintainsdatabases containing indicators of stability and occupationalinformation suitable for use in process 200. The lists and indicatorsmay need to be interpreted (i.e., cleaned and properly formatted) sothat they are suitable for use in process 200 (see, e.g., FIG. 3, datainterpreter 112 of FIG. 1, and related text).

Parameters may be also received and stored in memory 108 (step 202). Theparameters may be associated with, for example, risk band matrix 113.The parameters may define risk bands for each indicator of stability.Alternatively or additionally, the parameters may define risk bandsassociated with sub-combinations of the indicators. The number of riskbands and the parameters associated with each risk band may bedetermined by the financial account provider operating process 200.Alternatively or additionally, parameters that coincide with analysesconducted by other entities may be adopted so that the data provided byprocess 200 becomes more easily compared to other data. Other risk banddivisions advantageous to disclosed embodiments are possible. Thecreation of an exemplary risk band matrix is described in more detail inrelation to FIG. 4A, below.

Values of each of the indicators of stability for a particularoccupation may be compared to the indicator of stability parameters andassigned risk band scores for each indicator (step 203). The risk bandscores may be compiled to create an occupational risk score for thatoccupation. The compilation of the risk band scores may be weighted suchthat certain indicators of stability have a greater effect on the riskscore than other indicator of stability (i.e., the average educationattained risk band score may be weighted to have a greater effect on theoccupational risk score than the unemployment rate for that occupationfor a certain year or years). The calculation of exemplary occupationalrisk scores for certain occupations is described in more detail inrelation to FIG. 4B, below. Alternatively, in some embodiments, theoccupations are merely associated with one or more risk band scores,without further compilation of the risk band scores to develop theoccupational risk score.

In shown in step 203, the financial account provider system 104 may alsobe configured to receive occupation information from a customer or apotential customer (e.g., from customer 101 via client 102) or about acustomer or potential customer (e.g., from customer occupationinformation database 105). The occupations may be grouped according tooccupational risk scores. For example, the occupational risk scores maybe divided into quartiles, with the highest 25% of occupational riskscores associated with the least stable occupations and the highestcredit risk customers and the lowest 25% of occupational risk scoresassociated with the most stable occupations and the lowest credit riskcustomers. Alternatively, in some embodiments, the occupations aremerely associated with risk scores, without further grouping ofoccupations according to risk scores.

Financial account provider system 104 may be configured to receiveoccupation information from customer 101 (step 205). The formatting ofthe customer's occupation information is described in more detail inrelation to FIG. 5 below (see also FIG. 1 at occupation titleinterpreter 115 and related text). Financial account provider system 104may also be configured to match the customer's occupation to anoccupation in the list of occupations (step 206). Based on the match,the risk score associated with the stability of the customer'soccupation may be assigned to customer 101. According to the groupingsof occupations, the classification of the stability of the customer'soccupation may be assigned (step 207). Alternatively, based on thematch, the occupational risk score without a classification of theoccupation based on the score may be assigned to customer 101. As afurther alternative, based on the match, the risk band scores may beassigned to customer 101 without assigning an occupational risk score ora classification of the occupation according to the occupational riskscore.

In certain embodiments, the assigned occupational risk score (or riskband scores or classification of occupation based on occupational riskscore) may be used to determine creditworthiness (step 208). Indetermining creditworthiness, scores or classifications associated withmore stable occupations may be associated with lower credit risk andbetter creditworthiness. Similarly, scores or classifications associatedwith less stable occupations may be associated with higher credit riskand less creditworthiness. Alternatively, at step 208, the assignedscores or classifications may be used in conjunction with otherindicators of creditworthiness to determine the creditworthiness ofcustomer 101. Further details regarding the use of the scores orclassifications to determine creditworthiness and use with otherindicators of creditworthiness are detailed in relation to FIGS. 6A-7C.

FIG. 3 depicts a flowchart of an exemplary data interpreting processwhich may be used in process 200, including step 201 of FIG. 2.Financial account provider 110 may desire to use data interpretingprocess 300 to clean, format, and consolidate data received from varioussources (or from a single source) so that the data is easier tomanipulate. Occupational title data and indicators of stability data maybe obtained from free-form (unstructured) text that may havetypographical errors, abbreviations, employer names (instead ofoccupations), synonyms (that may allow consolidation into a singleoccupation title for the purposes of disclosed embodiments), nearhomonyms, or other errors that make consolidating the data difficult.Even data captured through dropdown menus or other static sets ofoccupational options may benefit from data interpretation so that datafrom various sources can more easily be combined. Data interpreter 112of financial account provider system 104 may be configured to providedata interpreting process 300.

Data interpreter 112 may also receive raw occupation title data andindicator of stability data (step 301). This data may be received fromdata miner 111, which may have obtained the data from occupationaldatabase 106. Words in occupation titles may be expanded to removeabbreviations based on lists of abbreviations, expansions, andsubstitutions in order to substitute the complete word for theabbreviated word. In certain embodiments, a list of stop words (i.e.,words that do not add special meaning such as “and,” “the,” etc.) and/ora list of special characters (e.g., #, $, %) may be used to remove stopwords and special characters from the data. One or more dictionaries mayalso be used to check the spelling of occupations (step 303), replacingthe misspellings with the correctly spelled occupation title.

The cleaning of the data may create a set of intermediate occupationtitle data and intermediate indicator of stability data allowing for thegrouping of data (step 304). Using natural language processing,synonymous or closely related occupation titles may be consolidated intoa single final occupation title. Using functions, which may be weighted,the intermediate indicator of stability data for the variousintermediate occupation titles may be consolidated so that the finaloccupation title is associated with a single set of indicators ofstability. For example, a “letter carrier” (intermediate occupationtitle) may be associated with an average annual salary of $35,000(intermediate indicator of stability) and a “mailman” (intermediateoccupation title) may be associated with an average annual salary of$37,000 (intermediate indicator of stability). The final occupationtitle may be “postal worker” and the final indicator of stabilityassociated with a postal worker may be an average salary of $36,000(see, e.g., step 305). Other functions besides straight averaging may beused to combine indicators of stability into final indicators ofstability. For example, weighted average functions may be used. Forinstance, if the average annual salary for five individual “lettercarriers” was $35,000, and the average annual salary for 15,000individual “letter carriers” was $37,000, the combination of these maybe weighted so that the final indicator of stability for “postalworkers” was $36,999 ((5 individuals×$35,000+15,000individuals×$37,000)/20,000 total individuals=$36,999 per individual).

In certain embodiments, in addition to consolidating similar occupationtitles into final occupation titles, a separate final occupation titleof sensitive occupation may be created. The sensitive occupation titlemay include occupation titles for which policies or laws may exist, andfor which financial account provider 110 may decide to treat differentlythan other occupations (for example, weighting the indicators ofstability differently, etc.). For example, due to anti-discriminationpolicies, data interpreter 112 may be configured to consolidate suchoccupation titles as retired, homemaker, housewife, and disabled into asensitive occupation title. Of course, other such consolidations basedon other criteria are contemplated in the disclosed embodiments, and oneof skill in the art would understand consolidations that would beadvantageous.

FIG. 4A depicts an exemplary risk band matrix, which may be used inprocess 200. Financial account provider system 104 may be configured tocreate and/or store risk band matrix 113. Risk band matrix 113 may becreated using occupations and indicator of stability data fromoccupational database 106, which may have been obtained using data miner111 and cleaned and formatted by data interpreter 112.

As shown in FIG. 4A, risk bands 408 may be created, providing theparameters for the indicators of stability. In certain embodiments, therange of indicators of stability for all occupations may be divided intoquartiles to create four risk bands. Risk bands for one or more incomeparameters (i.e., average annual income 401) may be provided. In theexemplary matrix shown in 4A, average annual income across alloccupations range from $0 to over $90,000. Risk band 1, associated withthe highest occupational stability and the lowest credit risk, mayrepresent the first quartile of all of the occupational income data(i.e., the top 25% of all of the occupations, which have an annualaverage income of over $90,000, rounded to the nearest $1,000). Riskband 2, associated with slightly lower stability and a slightly higherrisk than risk band 1, may represent the second quartile of all of theoccupational income data (i.e., the second 25% of all occupations, whichhave an annual average income of $63,000 to $89,000, rounded to thenearest $1,000). Similarly, risk band 3 of FIG. 4A may be associatedwith the third quartile (i.e., the third 25% of all occupations, whichhave an average annual income of $35,000 to $62,000). And risk band 4 ofFIG. 4A may be associated with the fourth quartile, the lowestoccupational stability and the highest credit risk (i.e., the bottom 25%of all occupations, which have an average annual income of $0-$34,000).

Risk bands for one or more education parameters (e.g., average education402) may also be provided, as shown in FIG. 4A. Risk band 1 for averageeducation may be associated with less than 3% of individuals with acertain occupation obtaining less than a high school diploma. Risk band1 may also be associated with greater than 65% of individuals withcertain occupation obtaining a masters degree or higher. Risk bands 2-4follow similarly. These two exemplary parameters are mutually exclusive,and risk band scores can be assigned for each. For example, if anoccupation requires a bachelors degree, but very few individuals obtainanything beyond the bachelors, then that occupation could be associatedwith a 1 risk band score in average education (for less than 3%obtaining less than a high school diploma) and a 5 risk band score inaverage education (for less than 1% obtaining more than a mastersdegree).

Risk bands for one or more unemployment parameters (i.e. unemployment405) may be provided. As shown, the unemployment rate for the lowestrisk, most stable occupations in 2009 (i.e., the top quartile) was 3.8%or lower. The highest risk, most unstable occupations in 2009 (i.e., thebottom quartile) was 14.7% or higher. FIG. 4A also provides historicaldata for the change in unemployment rates during an economic downturn.For risk band 1, the most stable occupations, the unemployment ratesonly fluctuated 1.9% or less from its lowest to highest unemploymentrates during a difficult economic period. For risk band 4, the mostunstable occupations, the unemployment rates fluctuated 8.5% or morefrom the lowest to highest unemployment rates during the same period.Again, these two unemployment parameters (2009 unemployment 406 andpeak-to-trough change 407) are mutually exclusive. Risk scores may beassigned to one without affecting the other. For example, if anoccupation is associated with high unemployment rates, but thatunemployment rate is generally steady over time and did not get worseduring the economic downturn, then that occupation could be assigned arisk band score of 4 in unemployment (for unemployment over 14.7% in2009) and 1 in unemployment (for peak-to-trough changes in unemploymentrates of less than 1.9%). Additional risk matrices may also beadvantageous to the disclosed embodiments.

FIG. 4B is an exemplary risk score chart that may be used in process200. The risk score engine may be configured to generate risk scorechart. Generation of the risk score chart may include using occupationtitle and indicator of stability data from occupational database 106,obtained by data miner 111, cleaned and formatted by data interpreter112, organized into a risk band matrix, and stored in risk band scorematrix 113.

To generate a risk score chart, the individual indicators of stability409 associated with a particular occupation 410 may be compared to therisk band matrix and assigned risk band scores 411. For example, asshown, a doctor (occupation) may earn more than $90,000 annually, onaverage, so an income risk band score of 1 may be assigned. Less than 3%of doctors may have less than a high school diploma and more than 65% ofdoctors may have a higher degree than a master's, so two educationsscores of 1 may be assigned. Doctors may have very low unemployment andmay continue to have low unemployment even in poor economic times, sotwo more scores of 1 are assigned for the unemployment scores. Similarcomparisons may be done for other occupations (such as constructionworker and teacher, as shown in chart 4B).

Occupational risk scores 412 may be generated by combining some or allof the risk band scores. As shown in FIG. 4B, risk band scores may beadded together to generate occupational risk scores 412. Alternatively,certain risk band scores may be given more weight or may be excludedfrom the occupational risk score calculation.

FIG. 5 is a flowchart of an exemplary occupation title interpreterprocess consistent with disclosed embodiments, including steps 105 and106 of process 200. Financial account provider 110 may desire to useoccupation title interpreter process 500 to clean, format, andconsolidate title data received from various sources (such as customer101 (via client 102) or customer occupational database 105) so that thedata is easier to manipulate. As noted above in relation to FIG. 3,occupational title data (regardless of the source) may containtypographical errors, abbreviations, etc. It may also be beneficial evenfor data that is free from errors to be processed through process 500 sothat the customer's occupation title is consolidated into one of theoccupational titles for which indicators of stability are known (i.e.,so that the natural language processing associates the customer'soccupation title with one of occupations in the list of occupations).

Occupation title interpreter 115 of financial account provider system104 may be configured to provide occupation title interpreting process500. Occupation title interpreter 112 may receive a raw occupation title(step 501). Using a list of abbreviations, expansions, andsubstitutions, words in occupation titles may be expanded to removeabbreviations, substituting the complete word for the abbreviated word.In certain embodiments, a list of stop words (i.e., words that do notadd special meaning, such as “and,” “the,” etc.) and list of specialcharacters (e.g., #, $, %) may be used to remove stop words and specialcharacters from the customer's occupation title. One or moredictionaries may also be used to check the spelling of the occupation,replacing the misspellings with the correctly spelled occupation title(step 503).

After the cleaning of the data, which may create an intermediatecustomer occupation title (step 504), natural language processingtechniques and/or comparison functions may be used to match thecustomer's occupation title to one of the occupation titles in the listof occupations for which indicators of stability, risk band scores,occupational risk scores, or categories of stability are known. Thismatched occupation title may be the final customer occupation title 505.

FIG. 6A is an example of orthogonal risk splitting between overallunstable occupations and overall stable occupations obtained by usingoccupational data to determine creditworthiness. Orthogonal risksplitting or risk splitting is a division or separation of data, oftendata with similar characteristics, based upon an additional criterion oradditional criteria. For example, as shown in FIG. 6A, account data thatmay be similar based on standard characteristics may show orthogonalrisk splitting when the additional criterion of occupational stabilityis applied. In the graph, historic data for 452,000 accounts fromcustomers in deciles 1-3 were analyzed, consistent with disclosedembodiments. Deciles 1-3 are the 30% of accounts deemed to have thelowest risk based on standard risk metrics (before occupational riskmetrics are applied). Using data from the Bureau of Labor andStatistics, various occupations were given occupation risk scores. Theoccupations of the customers associated with 452,000 accounts wereassociated with the various occupational risk scores, consistent withdisclosed embodiments. The top quartile of accounts, associated withoccupations with the lowest occupational risk scores, were deemed stableoccupation accounts. The bottom quartile of accounts, associated withoccupations with the highest occupational risk scores, were deemedunstable occupation accounts. Of the 452,000 accounts, 114,000 accountswere deemed stable occupation accounts and 22,400 were deemed unstableoccupation accounts. The historic cumulative percentage of accounts thathave charged off during a period of time (i.e., the total number ofaccounts over a period of time that have had at least one charge offduring that period of time divided by the total number of accounts atthe start of the study, or the percentage of bad accounts per originalaccounts, or the “cumulative Pbad/orig”) over a 61-month period wasgraphed for (1) all of the 452,000 accounts—the dotted line labeled‘Overall (452K)”; (2) the 114,000 stable occupation accounts—the lowersolid line labeled “Stable (114K)”; and (3) the 22,400 unstableoccupation accounts—the higher solid line labeled “UnStable (22.4K).” Asshown, even among customers provided lines of credit based oncreditworthiness calculations known in the art, further insight intorisk can be seen by applying the occupational risk classifications.Customers with occupations deemed unstable show less creditworthiness,as evidenced by the higher cumulative Pbad/orig rates as compared toboth the stable occupation accounts and the accounts overall. Andcustomers with occupations deemed stable show higher credit worthiness,as evidenced by the lower cumulative Pbad/orig rates as compared to boththe unstable occupation accounts and the accounts overall.

FIG. 6B is an example of the orthogonal risk splitting between unstableoccupations and stable occupations for occupations with high incomes(over $80K annually) that can be obtained by using occupational data todetermine creditworthiness. In this figure, the each line depicts thecumulative Pbad/orig for accounts of customers with annual incomes ofover $80,000. Even when normalizing the data by income (i.e., analyzingdata from customers with similar incomes), orthogonal risk splitting maybe observed. As shown in FIG. 6B, 9,700 accounts were associated withoccupations that were deemed unstable, and those accounts had a higherrate of cumulative Pbad/orig than 98,000 unclassified (Not class)accounts, and over double the rate of the 61,000 accounts thatassociated with stable occupations.

FIG. 7A is an exemplary graph of the Pbad per open for accountsassociated with FICO scores and occupation stability for customers withannual incomes of over $80,000. Consistent with disclosed embodiments,similar to as described in FIG. 6A, account data was analyzed based onoccupation. The top quartile of accounts was deemed stable occupationaccounts and the bottom was deemed unstable occupation accounts.Historical account data for the Pbad per open over 61 months was graphedfor (1) all accounts associated with customers with a FICO score above720; (2) all accounts associated with customers with a FICO score below720; (3) the 25% of accounts deemed stable occupation accounts; and (4)the 25% of accounts deemed unstable occupation accounts. As shown, thePbad per open rates for customers with stable occupations tend to alignwith the rates for customers with high FICO scores, and the rates forcustomers with unstable occupations tend to align with the rates forcustomers with low FICO scores. This indicates creditworthinessdeterminations based on occupational stability share similar predictivestrengths to traditional determinations of creditworthiness such as FICOscores.

FIG. 7B, however, indicates that the occupational stabilitycreditworthiness determinations may provide additional risk splittingwhen crossed with FICO scores. In other words, occupational risk doesnot merely mimic other creditworthiness determinations, but it is ableto add significant insight into who among those with similarcreditworthiness based on prior art methods are actually more or lesscreditworthy. FIG. 7B provides an exemplary graph of the distribution ofstable occupations (e.g., the top quartile and lowest risk occupations)and unstable occupations (e.g., the bottom quartile and highest riskoccupations) over FICO bands. That is, the percentage of accountsassociated with customers with stable occupations and the percentage ofaccounts associated with customers with unstable occupations were eachgraphed according to FICO score bands. As shown, stable and unstableoccupations can be found across all FICO score bands. Even amongindividuals with the same FICO scores, some will be employed in stableoccupations and some will be employed in unstable occupations.

FIG. 7C is an exemplary graph depicting the orthogonal splitting powerthe stability of occupation has on top of a FICO score in determiningcreditworthiness. The cumulative Pbad/orig rate for accounts associatedwith individuals with (1) low FICO scores and stable occupations, (2)low FICO scores and unstable occupations, (3) high FICO scores andstable occupations, and (4) high FICO scores and unstable occupationsare shown. Consistent with the data in FIGS. 7A and 7B, FIG. 7C showsthe splitting based on stability of incomes above and beyond thesplitting due to FICO score differences. Accounts associated withindividuals with low FICO scores had higher cumulative Pbad/orig ratesthan those with high FICO scores, regardless of the individual'soccupation (though it is understood that in certain circumstances,occupations may have more influence over cumulative Pbad/origrates thanFICO scores). But among those with low FICO scores, those with unstableoccupations had more than double the cumulative Pbad/orig rate thanthose with stable occupations. The same is true for those with high FICOscores: those with high FICO scores but unstable occupations had morethan double the cumulative Pbad/orig rate than those with stableoccupations.

The foregoing descriptions have been presented for purposes ofillustration and description. They are not exhaustive and do not limitthe disclosed embodiments to the precise form disclosed. Modificationsand variations are possible in light of the above teachings or may beacquired from practicing the disclosed embodiments. For example, thedescribed implementation includes software, but the disclosedembodiments may be implemented as a combination of hardware and softwareor in hardware alone. Additionally, although disclosed aspects aredescribed as being stored in a memory on a computer, one skilled in theart will appreciate that these aspects can also be stored on other typesof computer-readable media, such as secondary storage devices, like harddisks, floppy disks, a CD-ROM, or other forms of RAM or ROM. Inaddition, an implementation of software for disclosed aspects may useany variety of programming languages, such as Java, C, C++, JavaScript,or any other now known or later created programming language.

Other embodiments will be apparent to those skilled in the art fromconsideration of the specification and practice of the embodimentsdisclosed herein. It is intended that the specification and examples beconsidered as exemplary only, with the true scope and spirit beingindicated by the following claims. For example, in some embodiments, therisk band scores and occupational risk scores may be directly related tothe level of risk and inversely related to creditworthiness (such asshown in FIG. 4A), such that the lower the score, the lower the risk andthe higher the creditworthiness. Conversely, in some embodiments, therisk band scores and occupational risk scores may be inversely relatedto the level of risk and directly related to creditworthiness, such thatthe lower the score, the higher the risk and the lower thecreditworthiness. It is contemplated that these or other scoring schemesmay be employed, and one of skill in the art will be able to modify thedisclosure as appropriate and/or necessary to accommodate the scoringscheme used.

The disclosed embodiments may be performed with clients 102 that aremobile devices or tablets through mobile applications that providecommunications with the functionalities of financial account providersystem 104 as disclosed herein. In other embodiments, financial accountprovider system 104 may not be associated with any particular financialaccount provider 110 or any type of account provider, but rather it maybe associated with a third party system that is leveraged by accountprovider(s) determine the creditworthiness of customers 101. Forinstance, a business may implement the disclosed embodiments such thatit offers creditworthiness determining services for other businesses(e.g., financial account provider 110). For example, a business entitymay host and provide financial account provider system 104 and itsfunctionalities such that financial account provider system 104 performscreditworthiness determination processes on behalf of one or moreaccount providers. The hosting financial account provider system entitymay charge fees for such services, and those fees may be adjusted basedon the types of creditworthiness determination services provided, thenumber of creditworthiness determinations completed, or other feearrangements. In such embodiments, one or more of the creditworthinessdeterminations may be performed by financial account provider 110, whileothers performed by the third party financial account provider systemprovider. Alternatively, financial account provider system 104 mayperform all of the creditworthiness determinations for a customer 101and report results to a financial account provider 110 through knowncomputer systems and networking components.

In addition, the disclosed embodiments may not be limited to anyparticular type of account or device. That is, instead of financialaccounts, aspects of the disclosed embodiments may be implemented toprovide creditworthiness or risk assessments for any type of account(e.g., financial, memberships, utilities, phone services, etc.).Further, the disclosed embodiments may be provided as a one-stop onlinelocation for providing creditworthiness determinations for one or moredifferent types of accounts. Further, financial account provider system104 may provide creditworthiness determinations for businesses insteadof individuals, wherein the stability of a particular type of businessmay be used to determine the credit risk of that business (either usedalone or in combination with other known indicators ofcreditworthiness). One of skill in the art would understand the types ofindicators of stability for businesses that would be advantageous. Forexample, indicator of stability data on the average annual net revenuesfor a certain type of business may be used (instead of average annualincome for individuals), and average failure/bankruptcy rates may beused (instead of unemployment rates).

1-20. (canceled)
 21. A system for determining creditworthiness of acustomer, comprising: one or more storage devices storing instructions;and one or more processors configured to execute the instructions toperform the operations of: receiving a plurality of indicators ofstability of occupations; creating a plurality of intermediate customeroccupations based on the received occupations; creating a plurality ofintermediate indicators of stability of the intermediate customeroccupations, the intermediate indicators of stability being createdbased on the received indicators of stability; consolidating theintermediate customer occupations and the intermediate indicators ofstability to produce a plurality of consolidated customer occupations,the consolidated customer occupations being associated with consolidatedindicators of stability; assigning risk bands to the consolidatedcustomer occupations; generating occupational risk scores based on therisk bands; receiving customer occupation information; associating thecustomer occupation information with one of the consolidated customeroccupations; assigning an occupational risk score to the customer fromthe generated occupational risk scores based on the association;calculating creditworthiness of the customer based on the occupationalrisk score assigned to the customer; and reporting the creditworthinessof the customer to a financial account provider.
 22. The system of claim21, wherein the list of indicators of stability comprises at least oneof an average annual income for an occupation, an average highesteducational level attained for the occupation, a percentage of personswith different educational histories for the occupation, an averageunemployment rate for the occupation, or a stability of unemploymentrate for the occupation.
 23. The system of claim 21, wherein: assigningrisk bands further comprises assigning risk band values for the riskbands, a higher risk band value being associated with more risk than alower risk band value; and generating the occupational risk scores basedon the risk bands assigned to the consolidated customer occupationscomprises summing the risk band values.
 24. The system of claim 21,wherein: assigning risk bands to the consolidated customer occupationsfurther comprises assigning weighted risk band values for the riskbands, a higher risk band value being associated with more risk than alower risk band value; and generating the occupational risk scores basedon the risk bands comprises summing the weighted risk band values. 25.The system of claim 21, wherein creating the intermediate customeroccupations further comprises at least one of: expanding abbreviationscontained within customer occupation title information included in thecustomer occupation information; removing at least one of stop words orspecial characters from the customer occupation title information; orspell-checking the customer occupation title information.
 26. The systemof claim 21, wherein associating the customer occupation informationfurther comprises associating the customer occupation information withthe consolidated occupation that meets at least one of the followingcriteria: matches the consolidated customer occupation titleinformation; or is closely related to the consolidated customeroccupation title information.
 27. The system of claim 21, wherein theoperations further comprise calculating creditworthiness of the customerbased on the occupational risk score assigned to the customer bycombining the occupational risk score assigned to the customer with thecustomer's FICO score.
 28. The system of claim 21, wherein: the systemis associated with a third party business; and the operations furthercomprise providing the results of the creditworthiness calculation tothe financial service provider.
 29. The system of claim 21, wherein theoperations further comprise: retrieving indicators of stability datafrom at least one of a public or a private occupational database; andformatting the indicators of stability data to create the indicators ofstability.
 30. The system of claim 21, wherein operations furthercomprise classifying the consolidated occupations by grouping theconsolidated occupations with similar occupational risk scores into thesame risk class.
 31. The system of claim 30, wherein calculatingcreditworthiness of the customer based on the risk class assigned to thecustomer comprises combining the risk score assigned to the customerwith the customer's FICO score.
 32. A computer-implemented method fordetermining creditworthiness of a customer, comprising: receiving aplurality of indicators of stability of occupations; creating aplurality of intermediate customer occupations based on the receivedoccupations; creating a plurality of intermediate indicators ofstability of the intermediate customer occupations, the intermediateindicators of stability being created based on the received indicatorsof stability; consolidating the intermediate customer occupations andthe intermediate indicators of stability to produce a plurality ofconsolidated customer occupations, the consolidated customer occupationsbeing associated with consolidated indicators of stability; assigning,by at least one processor, risk bands to the consolidated customeroccupations; generating, by the at least one processor, occupationalrisk scores based on the risk bands; receiving customer occupationinformation; associating the customer occupation information with one ofthe consolidated customer occupations; assigning an occupational riskscore to the customer from the generated occupational risk scores basedon the association; calculating creditworthiness of the customer basedon the occupational risk score assigned to the customer; and reportingthe creditworthiness of the customer to a financial account provider.33. The method of claim 32, wherein the list of indicators of stabilitycomprises at least one of an average annual income for an occupation, anaverage highest educational level attained for the occupation, apercentage of persons with different educational histories for theoccupation, an average unemployment rate for the occupation, or astability of unemployment rate for the occupation.
 34. The method ofclaim 32, wherein: assigning risk bands further comprises assigning riskband values for the risk bands, a higher risk band value beingassociated with more risk than a lower risk band value; and generatingthe occupational risk scores based on the risk bands assigned to ofconsolidated customer occupations comprises summing the risk bandvalues.
 35. The method of claim 32, wherein: assigning risk bands to theconsolidated customer occupations further comprises assigning weightedrisk band values for the risk bands, a higher risk band value beingassociated with more risk than a lower risk band value; and generatingthe occupational risk scores based on the risk bands comprises summingthe weighted risk band values.
 36. The method of claim 32, wherein:creating the intermediate customer occupations further comprises:expanding abbreviations contained within customer occupation titleinformation included in the customer occupation information; removing atleast one of stop words or special characters from the customeroccupation title information; or spell-checking the customer occupationtitle information; and associating the customer occupation informationfurther comprises: associating the customer occupation information withthe consolidated occupation that meets at least one of the followingcriteria: matches the consolidated customer occupation titleinformation; or is closely related to the consolidated customeroccupation title information.
 37. The method of claim 32, furthercomprising: calculating creditworthiness of the customer based on theoccupational risk score assigned to the customer by combining theoccupational risk score assigned to the customer with the customer'sFICO score.
 38. The method of claim 32, further comprising: retrievingindicators of stability data from at least one of a public or a privateoccupational database; and formatting the indicators of stability datato create the indicators of stability.
 39. The method of claim 32,wherein calculating creditworthiness of the customer based on the riskclass assigned to the customer comprises combining the risk scoreassigned to the customer with the customer's FICO score.
 40. A tangiblecomputer-readable medium storing instructions which, when executed by aprocessor, perform operations for determining creditworthiness of acustomer, comprising: receiving a plurality of indicators of stabilityof occupations; creating a plurality of intermediate customeroccupations based on the received occupations; creating a plurality ofintermediate indicators of stability of the intermediate customeroccupations, the intermediate indicators of stability being createdbased on the received indicators of stability; consolidating theintermediate customer occupations and the intermediate indicators ofstability to produce a plurality consolidated customer occupations, theconsolidated customer occupations being associated with consolidatedindicators of stability; assigning risk bands to the consolidatedcustomer occupations; generating occupational risk scores based on therisk bands; receiving customer occupation information; associating thecustomer occupation information with a one of the consolidated customeroccupations; assigning an occupational risk score to the customer fromthe generated occupational risk scores based on the association;calculating creditworthiness of the customer based on the occupationalrisk score assigned to the customer; and reporting the creditworthinessof the customer to a financial account provider.